摘要
针对量子遗传算法不适于连续函数优化的问题,提出了一种改进的量子遗传算法。该算法直接将量子染色体与当前最优解相比较来确定旋转门的旋转角,种群中各个体以不同速率向最优解进化以同时实现全局搜索与局部搜索,引入变异操作以防止算法早熟收敛。对该算法及其全局收敛性进行了分析后,将其用于函数极值求解与PID控制器的参数优化,并与遗传算法和量子遗传算法进行比较。仿真结果表明该算法具有较好的寻优性能。
Aiming at the issue that quantum genetic algorithm is not suitable for continuons function optimization,an improved quantum genetic algorithm is proposed.This algorithm directly compares the quantum chromosomes with the current best solution to determine the rotating angle of the rotating gate.Every individual in population evolves with different rates to complete local search and global search simultaneously.Mutation operation is used to prevent the premature convergence.After analyzing the algorithm and its global convergence,this algorithm is applied to solve the function extremum and to optimize the PID controller parameters,and it is compared with the standard genetic algorithm and the quantum genetic algorithm.Experimental results illustrate that the proposed algorithm has better performance.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2010年第10期2219-2222,共4页
Systems Engineering and Electronics
基金
国家自然科学基金(60773065)资助课题
关键词
量子遗传算法
量子旋转门
全局收敛性
函数极值优化
PID参数优化
quantum genetic algorithm（QGA）
quantum rotating gate
global convergence
function extremum optimization
PID parameters optimization
